Integrated scheduling of cargo vessels, research vessels, and marine experiments in multifunctional ports using Q-learning enhanced PSO

• A hierarchical spatiotemporal scheduling mechanism is designed to resolve conflicts between vessels and marine experiments. • The novel BCAEA model is constructed to minimize the vessel turnaround time, crane movement distance, and experiment completion time. • The QLEPSO algorithm is proposed by integrating the position update strategy pool, Q-learning strategy selection, and adaptive parameter control. • The BCAEA_QLEPSO method is established and compared with FCFS to verify its effectiveness and superiority. Multifunctional ports integrating cargo and research operations (CRPs) face unprecedented scheduling complexities due to spatiotemporal conflicts among cargo vessels, research vessels, and marine experiments. To resolve the aforementioned resource conflicts, this study proposes a hierarchical spatiotemporal coordination framework that establishes differentiated operational zones and experiment time windows. Then, a multi-objective joint scheduling model (BCAEA) is formulated to integrate berth allocation, quay crane assignment, and experiment arrangement, simultaneously minimizing shipowners' and operational costs while maximizing experimental efficiency. To solve this large-scale optimization problem, an enhanced particle swarm optimization algorithm (QLEPSO) is developed, incorporating a position update strategy pool, Q-learning-based strategy selection, and adaptive parameter control. Numerical experiments using real operational data from Chinese CRPs demonstrate that QLEPSO outperforms standard PSO by 47.17% in solution quality for large-scale problems. Moreover, the proposed BCAEA_QLEPSO method generates high-quality allocation schemes for instances involving 90 vessels and 18 experiments within 1 minute, validating the effectiveness of integrating reinforcement learning with swarm intelligence for complex port scheduling.